Research Data Policy
In accordance with COPE’s Data and Reproducibility policy, ARSL encourages authors to share relevant data, code, and materials, register clinical trials, and use standardized guidelines to enhance the transparency, reproducibility, and credibility of scientific discoveries. Therefore, we encourage authors to deposit datasets in data repositories, provided that such data are not suitable for submission as online supplementary files. Authors who have deposited raw data in community database repositories are encouraged to include a data availability statement in their manuscripts. This statement should provide information about the availability of research data and any restrictions or conditions for accessing the data, except for reasonable controls related to human privacy or biosecurity. The reuse of scientific data has significant potential to advance scientific and economic development.
1. Data Sharing
For data sharing, the FAIR Data Principles should be followed, which guides the assignment of a globally unique and persistent identifier to (meta)data. Authors shall cite the correct sources. Collaborative mechanisms should be implemented between journals and institutions to monitor and ensure the scientific validity and credibility of overall research practices. Authors are encouraged to prioritize the use of original data in their research and provide supporting data, such as accessible data sources, at the earliest opportunity.
For data involving confidentiality/privacy/personal privacy, authors are advised to make every effort to anonymize identifiable sensitive information and share data in strict accordance with mandatory guidelines in the discipline.
In accordance with COPE’s guidelines on Unpublished Data, the journal will communicate with data providers regarding issues of scientific rigor in unpublished datasets. The journal will contact the corresponding author, requesting a response to the relevant issues and, if necessary, supporting documentation and information on any other affected content. In accordance with guidelines on Published Data, if the scientific rigor of a published dataset associated with a manuscript is questioned, the journal will contact all relevant journals that have published results from the questionable dataset, outlining the issue and measures taken to date. Authors must provide satisfactory updates. If the issue is significant or affects the manuscript’s conclusions, authors should retract the manuscript; otherwise, the journal will reject it.
2. Data Citation
We encourage authors to cite any datasets mentioned in the text that are deposited in external databases in the references. For published datasets, authors should cite both the published research article and the dataset source. The journal editorial office will check and ensure the accuracy of data citations before publication.
Data citations should include the minimum information recommended by DataCite:
- Author(s)
- Year of publication/distribution
- Title
- Publisher/Repository or archive name
- Persistent identifier (e.g., DOI)
3. Data Repositories
We encourage authors to deposit datasets in relevant community data repositories or select general-purpose data repositories that comply with disciplinary norms and requirements, including any general data repositories provided by universities, funding agencies, or research institutions for their affiliated researchers. Publishers recommend that authors choose repositories with Digital Object Identifiers (DOIs) to ensure that cited datasets can be consistently linked.
Authors may find lists of registered data repositories through online resources such as FAIRsharing.org and re3data.org.
Examples of some general-purpose data repositories include:
· Open Science Framework
· Dryad Digital Repository
· Figshare
· Harvard Dataverse
· Zenodo
· Science Data Bank
